Generative AI / Agentic AI Engineer (AWS)

About The Role We are looking for a skilled Generative AI / Agentic AI Engineer with strong expertise in AWS cloud services to design, develop, and deploy production-grade AI applications. The ideal candidate should have hands-on experience building LLM-powered applications, AI agents, RAG pipelines, and scalable cloud-native solutions on AWS. Key Responsibilities Design and develop Generative AI and Agentic AI applications using Large Language Models (LLMs). Build AI agents capable of reasoning, planning, tool usage, and workflow automation. Develop Retrieval-Augmented Generation (RAG) pipelines using vector databases. Integrate LLMs through APIs such as OpenAI, Anthropic Claude, Amazon Bedrock, or open-source models. Deploy scalable AI applications using AWS cloud services. Build REST APIs and backend services for AI-powered applications. Design prompt engineering strategies and optimize model performance. Monitor, evaluate, and improve AI model quality, latency, and cost. Collaborate with Product, Data Science, and Engineering teams to deliver AI solutions. Ensure security, scalability, and best practices for cloud-native AI applications. Mandatory Skills 3+ years of software development experience. Hands-on experience with Generative AI and Agentic AI development. Strong knowledge of LLMs (GPT, Claude, Llama, Mistral, etc.). Experience with RAG (Retrieval-Augmented Generation). Experience with AI orchestration frameworks such as: LangChain LangGraph LlamaIndex CrewAI AutoGen (preferred) Strong programming skills in Python. Hands-on experience with AWS services including: Amazon Bedrock Lambda API Gateway ECS/EKS EC2 S3 IAM CloudWatch DynamoDB or RDS Experience with Docker and containerized deployments. Experience integrating REST APIs and third-party services. Familiarity with Git and CI/CD pipelines. Preferred Skills Experience with MCP (Model Context Protocol). Experience building multi-agent systems. Knowledge of vector databases such as Pinecone, Weaviate, Milvus, FAISS, or OpenSearch. Experience with embedding models and semantic search. Experience deploying open-source LLMs. Knowledge of Kubernetes. Experience with Terraform or AWS CloudFormation. Familiarity with MLOps and LLMOps practices. Experience with monitoring tools such as LangSmith, Phoenix, or MLflow. Qualifications Bachelor's or Master's degree in Computer Science, Information Technology, Artificial Intelligence, or a related field. AWS Certification (Associate or Professional) is a plus. Mandatory Screening Criteria Mandatory (Experience) Must have 3+ years of software development experience. Must have 1+ year of hands-on experience building production Generative AI or Agentic AI applications. Mandatory (Technical Skills) Strong Python programming. Hands-on experience with LLMs. Experience with RAG. Experience with LangChain, LangGraph, LlamaIndex, CrewAI, or AutoGen. Strong AWS experience, preferably with Amazon Bedrock. Experience deploying applications on AWS. Preferred MCP (Model Context Protocol) Multi-Agent AI systems Vector Databases Prompt Engineering LLM Evaluation Kubernetes Docker CI/CD Terraform MLOps/LLMOps Skills: bedrock,agentic ai,aws,gen ai,amazon,cloud,models